remove unnecessary columns

create_valid_csv.py

@@ -50,37 +50,40 @@ if __name__ == "__main__":
     output_json = "files.json"
     output_csv = "metadata-valid.csv"
     metadata_csv = "metadata-balanced.csv"
-    models = ["commonvoice", "metavoice", "playht", "stylettsv2", "xttsv2"]
+    sources = ["commonvoice", "metavoice", "playht", "stylettsv2", "xttsv2"]
+    columns_to_keep = ["path", "age", "gender", "accents", "sentence"]  # from the original metadata of commonvoice
+
 
     # Load the metadata
     df = pd.read_csv(metadata_csv)
+    df = df[columns_to_keep]
 
     # Create the JSON file
-    data = create_json(df, models, output_json)
+    data = create_json(df, sources, output_json)
 
     # Get paths that are only available for all models
-    valid_paths = [path for path in data if len(data[path]) == len(models)]
+    valid_paths = [path for path in data if len(data[path]) == len(sources)]
 
     # Filter dataframe to only include valid paths
     valid_df = df[df.path.isin(valid_paths)]
 
     # Create an entry for each model in csv
     all_dfs = []
-    for model in models:
-        valid_df_model = valid_df.copy()
-        valid_df_model["source"] = model
-        all_dfs.append(valid_df_model)
+    for source in sources:
+        valid_df_source = valid_df.copy()
+        valid_df_source["source"] = source
+        all_dfs.append(valid_df_source)
 
         # Add is_cloned_voice column
-        is_cloned_voice = model != "commonvoice"
-        valid_df_model["is_cloned_voice"] = is_cloned_voice
+        is_cloned_voice = source != "commonvoice"
+        valid_df_source["is_cloned_voice"] = is_cloned_voice
 
         # Add fname column
-        valid_df_model["filename"] = valid_df_model["path"]
+        valid_df_source["filename"] = valid_df_source["path"]
 
         # Add path column
-        valid_df_model["path"] = valid_df_model["path"].apply(
-            lambda path: os.path.join(model, path)
+        valid_df_source["path"] = valid_df_source["path"].apply(
+            lambda path: os.path.join(source, path)
         )
 
     all_df = pd.concat(all_dfs, ignore_index=True)

metadata-valid.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cf161465f3cc561871b02471f833fada89a767b6546ab48c0026696a0fd8b10b
-size 1814016
+oid sha256:2d078991bedfe4aecaca197f17f857bd395c80a3cfc39dab4a7330fa42834059
+size 849080